A machine tool machining parameter adjustment method and system

Abstract

The application provides a machine tool machining parameter adjusting method and system, and relates to the technical field of machine tool machining parameter adjustment.The application emits sound waves in front of the tool cutting workpiece material and receives echo signals, extracts acoustic characteristics, and real-time infers the local acoustic impedance of the workpiece material.When the local acoustic impedance is continuously higher than the preset normal reference value and exceeds the preset threshold value, it is determined that there is a local abnormal area, and a warning signal is generated.Before the tool contacts the local abnormal area, the machining parameters are adjusted in advance according to the warning signal.The real-time feedback signal of the cutting process is continuously monitored, and the machining parameters are further adjusted based on the real-time feedback signal and the warning signal.The application can effectively solve the problems of unexpected tool wear, machining size deviation accumulation and the like caused by uneven hardness of the workpiece material in the prior art.

Description

Technical Field

[0001] This application relates to the field of machine tool machining parameter adjustment technology, and more specifically, to a method and system for adjusting machine tool machining parameters. Background Technology

[0002] In the process of machining key components in modern high-precision machine tools, uneven hardness in the workpiece is a common challenge: when the tool cuts into such areas, the cutting force instantaneously exceeds the preset threshold of the sensor. Although the system reduces the feed and load, the cutting edge of the tool is still subjected to instantaneous stress far exceeding the fatigue limit due to the continuous change in hardness, which causes microscopic chipping or rapid wear, far exceeding the prediction range of conventional wear models.

[0003] Traditional tool compensation relies on preset models to adjust the path. However, the actual wear deviates significantly from the prediction, and compensation can actually increase dimensional errors. Furthermore, the parameter adjustment cycle is long, making it impossible to respond promptly to accelerated tool wear and material changes, leading to a continuous accumulation of deviations. When operators spot surface defects during inspections, they may misjudge them as "chatter" and manually adjust the spindle speed, disrupting the system's optimized spindle-feed speed matching relationship—the foundation of automatic parameter adjustment.

[0004] Once the correlation between system parameters is broken, it becomes impossible to accurately assess the state and find the optimal processing point, ultimately leading to a dilemma of "obvious symptoms but complex root causes": poor surface quality, compensation failure, and manual intervention exacerbating the problem, making it difficult to quickly locate and resolve the issue using conventional methods. Summary of the Invention

[0005] This application provides a method and system for adjusting machine tool machining parameters, aiming to solve the problems in the prior art where the machine tool machining parameter adjustment method is prone to unexpected rapid wear or micro-chipping of the tool when facing uneven local hardness inside the workpiece material, resulting in the accumulation of machining dimensional deviations, and the operator's manual intervention may disrupt the system's optimized matching relationship, ultimately affecting machining efficiency and workpiece quality.

[0006] On the one hand, this application provides a method for adjusting machine tool processing parameters, including:

[0007] A sound wave is emitted to the workpiece material in front of the cutting tool through a sound wave transmitting and receiving device and the echo signal is received. The acoustic features of the echo signal are extracted and the local acoustic impedance of the workpiece material is inferred in real time based on the acoustic features.

[0008] The local acoustic impedance is compared with a preset normal reference value. When the local acoustic impedance is found to be continuously higher than the normal reference value and exceeds a preset threshold, it is determined that there is a local abnormal area, and a warning signal containing location information and the degree of hardness abnormality is generated.

[0009] Before the cutting tool contacts the local abnormal area, the machining parameters are adjusted in advance based on the warning signal;

[0010] The machining parameters are further adjusted based on the real-time feedback signals of the cutting process and the early warning signals.

[0011] Optionally, the acoustic wave transmitting and receiving device includes a pair of piezoelectric transducers, which serve as the transmitter and receiver, respectively. They are fixed near the machine tool spindle tool holder and kept in a relatively fixed position relative to the tool, facing the workpiece material surface to be cut. The acoustic wave is an ultrasonic pulse with a frequency in the range of 1 MHz to 10 MHz.

[0012] Optionally, the acoustic features include sound wave attenuation, frequency shift, and phase change, and the step of extracting acoustic features from the echo signal includes:

[0013] The acoustic features are extracted by calculating the acoustic attenuation rate based on the echo signal using a high-speed analog-to-digital converter and a digital signal processor, performing a fast Fourier transform to analyze the frequency shift, and analyzing the phase difference.

[0014] Optionally, the step of adjusting the machining parameters in advance based on the warning signal before the tool contacts the local abnormal area includes:

[0015] Micro-area optical spectroscopy detection was performed in the close proximity region of the cutting edge of the tool to obtain the spectral peak position shift and the change in spectral peak intensity ratio in the spectral data;

[0016] Analyze the spectral peak position shift and the change in spectral peak intensity ratio to identify the phase transition induced by cutting stress;

[0017] Based on the phase transformation induced by the cutting stress, the degree of change in the actual hardness of the material during the cutting process is obtained, and the actual hardness value is obtained.

[0018] Based on the actual hardness value, calculate the target feed rate and speed transition curve parameters;

[0019] Adjust the toolpath based on the target feed rate and the speed transition curve parameters.

[0020] Optionally, the step of continuously monitoring the real-time feedback signal of the cutting process and further adjusting the machining parameters based on the real-time feedback signal and the early warning signal includes:

[0021] Monitor real-time feedback signals during the cutting process;

[0022] The real-time feedback signal is filtered to obtain the target real-time feedback signal;

[0023] Based on the real-time feedback signal from the target and the early warning signal, abnormal patterns in the processing status are identified, and identification results are obtained.

[0024] Based on the identification results, a parameter adjustment instruction is generated, and the processing parameters are further adjusted according to the parameter adjustment instruction.

[0025] Optionally, the step of performing micro-area optical spectral detection in the close proximity region of the cutting edge of the tool to obtain the spectral peak position shift and the change in spectral peak intensity ratio in the spectral data includes:

[0026] Laser pulses are emitted using an ultrashort pulse laser;

[0027] Establish a clock synchronization mechanism between the laser pulse trigger signal and the gated detector to achieve precise synchronization between laser pulse emission and detector activation;

[0028] Based on the precise synchronization of laser pulse emission and detector activation, the gated detector is activated to collect the optical signal within a very short time after the laser pulse reaches the material and generates a Raman scattering signal.

[0029] The gated detector is turned off at other times after laser pulse excitation.

[0030] The optical signals collected by the gated detector are analyzed for spectral peak position shift and spectral peak intensity ratio change to obtain the spectral peak position shift and spectral peak intensity ratio change in the spectral data.

[0031] Optionally, the step of performing micro-area optical spectral detection in the immediate vicinity of the cutting edge of the tool includes:

[0032] A high-speed airflow jet device is deployed along the path of the laser pulse emitted by the laser.

[0033] Within 0.5 to 0.1 milliseconds before the laser pulse is emitted, the high-speed airflow jet device sprays high-pressure inert gas into the laser beam propagation path to form a locally clean channel free of chips and liquid film.

[0034] A high-speed airflow jet device is deployed along the path of the fiber optic probe receiving Raman scattered light;

[0035] Within 0.5 to 0.1 milliseconds before the gated detector is activated, the high-speed airflow jet device injects high-pressure inert gas into the optical signal return path to form a locally clean channel free of chips and liquid film.

[0036] Transparent protective windows are installed at both the laser transmitter and the fiber optic probe receiver.

[0037] A miniature heating unit is provided inside the transparent protective window, and the surface temperature of the transparent protective window is maintained above the boiling point of the cutting fluid or the melting point of the chips by means of the miniature heating unit.

[0038] Optionally, the step of installing transparent protective windows at the laser transmitter and the fiber optic probe receiver respectively further includes:

[0039] The surface of the transparent protective window is laser polished and a replaceable sacrificial layer is provided so that the sacrificial layer is replaced when the wear of the sacrificial layer reaches a preset threshold.

[0040] Vibration and temperature sensors are integrated into the mounting base of the transparent protective window to monitor the vibration state and temperature changes of the transparent protective window.

[0041] Adjust the processing parameters when abnormal vibration or temperature occurs.

[0042] Optionally, the step of performing laser polishing on the surface of the transparent protective window includes:

[0043] A nanosecond-level pulsed laser is used to emit laser pulses, and the laser beam is controlled to scan an area on the surface of the transparent protective window.

[0044] Real-time monitoring of the temperature feedback signal on the surface of the transparent protective window;

[0045] Based on the temperature feedback signal, the laser power and scanning speed are dynamically adjusted.

[0046] On the other hand, this application provides a machine tool machining parameter adjustment system, the system comprising:

[0047] The detection module is used to emit sound waves to the workpiece material in front of the cutting tool through a sound wave transmitting and receiving device and receive the echo signal, extract acoustic features from the echo signal, and infer the local acoustic impedance of the workpiece material in real time based on the acoustic features.

[0048] The identification module is used to compare the local acoustic impedance with a preset normal reference value. When the local acoustic impedance is found to be continuously higher than the normal reference value and exceeds a preset threshold, it is determined that there is a local abnormal area and a warning signal containing location information and the degree of hardness abnormality is generated.

[0049] The pre-adjustment module is used to pre-adjust machining parameters based on a warning signal before the tool contacts the local abnormal area;

[0050] An adaptive adjustment module is used to continuously monitor real-time feedback signals during the cutting process, and further adjust machining parameters based on the real-time feedback signals and early warning signals.

[0051] This application relates to a machine tool machining parameter adjustment method and system, which effectively solves problems in existing technologies such as unexpected tool wear, accumulation of machining dimensional deviations, and operator intervention disrupting the system's optimized matching relationship due to uneven local hardness of the workpiece material. This method utilizes acoustic detection technology to achieve early detection and warning of potential abnormal hardness areas within the workpiece material. This allows the machine tool to pre-adjust machining parameters based on the warning information before the tool contacts the abnormal area, effectively avoiding microscopic chipping or rapid wear of the tool caused by instantaneous high stress when encountering hard areas. Simultaneously, further adaptive adjustments are made using real-time feedback signals during the cutting process, ensuring machining stability and workpiece quality, and avoiding the accumulation of dimensional deviations caused by response lag in traditional methods. Therefore, this application overcomes the shortcomings of existing technologies in timely and accurate response to local material inhomogeneities, significantly improving the intelligence level of machine tool machining, tool life, and workpiece machining accuracy. Attached Figure Description

[0052] To illustrate this application more clearly, the accompanying drawings used in the embodiments will be briefly described below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0053] Figure 1 The diagram above illustrates a process flow chart for adjusting machine tool machining parameters.

[0054] Figure 2 The diagram above illustrates a schematic of a machine tool machining parameter adjustment system.

[0055] Reference numerals: 100, Machine tool processing parameter adjustment system; 10, Detection module; 20, Identification module; 30, Pre-adjustment module; 40, Adaptive adjustment module. Detailed Implementation

[0056] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0057] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0058] In modern industrial production, the setting and adjustment of machining parameters for high-precision machine tools directly determine product quality and production efficiency when processing key components. However, actual production often encounters challenges that exceed expectations, such as uneven hardness within the workpiece material. When the machine tool's cutting tool suddenly encounters these areas of abnormally high hardness within the workpiece during machining, the cutting resistance between the tool and the workpiece increases instantaneously, causing microscopic chipping of the cutting edge or rapid wear of the tool material, thus affecting machining accuracy and surface quality. Traditional methods are insufficient to effectively address such sudden and localized material anomalies, resulting in both machining efficiency and workpiece quality failing to meet expectations.

[0059] like Figure 1 The diagram illustrates a flowchart of a method for adjusting machine tool machining parameters. This application proposes a method for adjusting machine tool machining parameters, comprising:

[0060] S10, a sound wave is emitted to the workpiece material in front of the cutting tool through a sound wave transmitting and receiving device and the echo signal is received. The acoustic features of the echo signal are extracted and the local acoustic impedance of the workpiece material is inferred in real time based on the acoustic features.

[0061] A sound wave transmitting and receiving device refers to a device used to transmit sound waves and receive their echo signals. This device typically contains one or more transducers that can convert electrical energy into acoustic energy and transmit it, while also converting the received acoustic energy back into electrical energy. Its function is to non-destructively probe the internal physical properties of a workpiece material, particularly changes in acoustic impedance. Acoustic characteristics refer to parameters extracted from the received echo signals that reflect the acoustic properties of the material. These characteristics are the basis for inferring the local acoustic impedance of the material. Local acoustic impedance is a measure of a material's ability to impede the propagation of sound waves and is related to the material's density and the speed of sound. When there is an anomaly in hardness within the material, its local acoustic impedance will change; therefore, by inferring the local acoustic impedance in real time, the local hardness of the material can be indirectly reflected.

[0062] S20, compare the local acoustic impedance with a preset normal reference value. When it is detected that the local acoustic impedance is continuously higher than the normal reference value and exceeds a preset threshold, it is determined that there is a local abnormal area, and a warning signal containing location information and the degree of hardness abnormality is generated.

[0063] The warning signal is a notification generated when a localized abnormal area is detected in the workpiece material. This signal includes not only the location of the abnormal area but also the degree of hardness abnormality, providing a basis for subsequent adjustments to processing parameters.

[0064] S30, Before the cutting tool contacts the local abnormal area, the machining parameters are adjusted in advance according to the warning signal;

[0065] Machining parameters refer to various adjustable process parameters during machine tool processing, such as spindle speed, feed rate, depth of cut, and toolpath. By adjusting these parameters, the cutting process can be optimized, improving machining quality and efficiency.

[0066] S40: Continuously monitor the real-time feedback signal of the cutting process, and further adjust the machining parameters based on the real-time feedback signal and the early warning signal.

[0067] This application introduces acoustic detection technology to predict localized hardness anomalies within the workpiece material before the cutting tool contacts it. Based on this, machining parameters are pre-adjusted, effectively avoiding tool damage and machining quality issues caused by passive adjustments after direct contact with the abnormal area in traditional methods. Furthermore, by combining real-time feedback signals during the cutting process, this application enables further adaptive adjustments to machining parameters, ensuring the stability and efficiency of the machining process.

[0068] The implementation environment of this application is typically a machine tool equipped with a CNC system, which can receive and execute parameter adjustment instructions, and has data acquisition and processing capabilities to monitor and analyze acoustic signals and real-time feedback signals of the cutting process.

[0069] First, a sound wave transmitter and receiver is used to emit sound waves towards the workpiece material in front of the cutting tool and receive the echo signals. Acoustic features are extracted from the echo signals, and based on these features, the local acoustic impedance of the workpiece material is inferred in real time. One implementation method is to use an independent ultrasonic probe, which scans in front of the cutting path via a robotic arm or guide rail system, emitting ultrasonic pulses and receiving the reflected echo signals. The probe can be integrated near the machine tool spindle, enabling continuous detection of the workpiece material through synchronous movement with the spindle. After the echo signals are acquired, they can be analyzed by a dedicated signal processing unit, for example, by calculating the amplitude attenuation, frequency spectrum changes, and signal propagation time to extract acoustic features. These acoustic features are then input into a pre-trained model that infers the local acoustic impedance of the workpiece material in real time based on the relationship between the material's acoustic properties and hardness. For example, a model based on machine learning algorithms can be used, trained with extensive experimental data to establish a mapping relationship between acoustic features and local acoustic impedance.

[0070] Secondly, the local acoustic impedance is compared with a preset normal reference value. When the local acoustic impedance is found to be consistently higher than the normal reference value and exceeds a preset threshold, a local abnormal region is identified, and a warning signal containing location information and the degree of hardness abnormality is generated. In one implementation, normal acoustic impedance reference values ​​and their fluctuation ranges for various common workpiece materials can be pre-stored. When the real-time inferred local acoustic impedance is compared with these reference values, if it is found to continuously deviate from the normal range and the degree of deviation exceeds the preset threshold, the region is identified as a local abnormal region. For example, a dynamic threshold can be set, which is adjusted based on the average acoustic impedance and standard deviation of the currently processed material. Once an abnormality is identified, a warning signal is immediately generated. This signal not only contains the precise location information of the abnormal region in the workpiece coordinate system but also the degree of hardness abnormality in the region, expressed as a percentage or grade, for example.

[0071] Secondly, before the tool contacts the abnormal area, machining parameters are pre-adjusted based on the warning signal. One implementation method is that after the warning signal is generated, the machine tool's CNC system calculates the time required for the tool to reach the abnormal area based on the position information contained in the warning signal. Within this time window, relevant machining parameters are pre-adjusted according to the degree of hardness abnormality indicated in the warning signal. For example, if the warning signal indicates a high-hardness area, the tool feed rate can be reduced, the depth of cut decreased, or the spindle speed adjusted to reduce the cutting load and prevent excessive impact on the tool when it contacts the abnormal area. This pre-adjustment can be completed before the tool actually contacts the abnormal area, effectively avoiding instantaneous tool damage.

[0072] Finally, the real-time feedback signals of the cutting process are continuously monitored, and the machining parameters are further adjusted based on these signals and early warning signals. In one implementation, the machine tool continuously monitors various real-time feedback signals during the cutting process, such as cutting force, spindle power, vibration signals, and tool wear status. These signals are acquired in real time by sensors and transmitted to the control system. The control system comprehensively analyzes these real-time feedback signals along with previously generated early warning signals. For example, even after pre-adjustments, if the real-time cutting force still shows abnormal fluctuations, or if the tool wear sensor indicates accelerated wear, the machining parameters will be further finely adjusted based on information about the hardness of the abnormal area in the early warning signal. This adjustment can be dynamic, for example, through fuzzy control or adaptive control algorithms, fine-tuning the feed rate or spindle speed according to the changing trend of the real-time feedback signals to ensure the stability of the cutting process and machining quality.

[0073] The machine tool machining parameter adjustment method proposed in this application combines acoustic detection technology with real-time cutting feedback to form a closed loop for machining parameter adjustment that is both forward-looking and adaptive.

[0074] Specifically, before or during machining, the acoustic wave transmitting and receiving device continuously performs non-destructive testing on the workpiece material in front of the cutting tool. When the acoustic waves penetrate the material and return, their echo signals carry information about the material's internal structure and hardness. By extracting acoustic features from these echo signals, such as analyzing acoustic wave attenuation, frequency shift, and phase changes, the local acoustic impedance of the workpiece material can be inferred in real time. This step is crucial to the entire method, enabling the detection of potential anomalies within the material before the tool actually contacts it.

[0075] Once the local acoustic impedance is inferred, it is compared with a preset normal reference value. If the local acoustic impedance remains higher than the normal reference value and exceeds a preset threshold, this usually indicates an abnormal increase in material hardness in that area. At this point, a local abnormal region is immediately identified, and a warning signal containing the precise location information of the region and the degree of hardness abnormality is generated. This warning signal provides clear guidance for subsequent parameter adjustments, avoiding the drawback of traditional methods that only respond passively after the tool contacts the abnormal region.

[0076] Before the cutting tool contacts a localized abnormal area, machining parameters are pre-adjusted based on the location and hardness information of the abnormal area provided in the warning signal. For example, if the warning signal indicates the presence of a high-hardness area ahead, the feed rate can be reduced, the depth of cut decreased, or the spindle speed adjusted in advance, thus effectively reducing the cutting load when the tool enters the area. This proactive adjustment significantly reduces the instantaneous impact on the tool when encountering hard areas, effectively extending tool life and preventing a decline in machining quality due to tool damage.

[0077] During the cutting process, real-time feedback signals such as cutting force, spindle power, and vibration are continuously monitored. These signals reflect the current cutting status. These real-time feedback signals are then comprehensively analyzed along with previously generated warning signals. For example, even after pre-adjustments, if the real-time cutting force still shows abnormal fluctuations, or if the tool wear sensor indicates accelerated wear, the machining parameters are further finely adjusted based on information about the hardness of abnormal areas from the warning signals. This adaptive adjustment mechanism ensures that even under complex machining conditions, optimization can be performed according to the actual situation, maintaining the stability and efficiency of the machining process.

[0078] The core innovation of this application lies in the introduction of a strategy combining forward-looking acoustic wave detection technology with adaptive real-time feedback adjustment. Compared with existing technologies, the advantages of this application are reflected in the following aspects:

[0079] First, this application utilizes an acoustic wave transmitting and receiving device to infer local acoustic impedance in real time within the workpiece material ahead of the cutting tool, thereby enabling the pre-identification of localized abnormal regions within the workpiece material. This proactive detection mechanism allows for the acquisition of early warning signals regarding the location and degree of hardness abnormality of the abnormal region before the tool contacts it. This contrasts sharply with existing technologies that only respond passively after an abnormal increase in cutting force, significantly reducing the instantaneous impact on the tool when encountering hard regions and effectively preventing premature tool damage.

[0080] Secondly, based on the early warning signal, this application can pre-adjust machining parameters before the tool contacts a local abnormal area. For example, the feed rate can be reduced or the spindle speed adjusted in advance. This pre-adjustment mechanism ensures that the cutting load is optimized when the tool enters the abnormal area, thereby significantly extending tool life and ensuring the smoothness of the machining process. This solves the problem of rapid tool wear or chipping due to instantaneous high stress in the prior art.

[0081] Furthermore, this application continuously monitors real-time feedback signals during the cutting process and further adjusts machining parameters based on these signals and early warning signals. This adaptive adjustment mechanism enables more refined and dynamic parameter optimization based on the actual cutting conditions and pre-obtained material anomaly information. It overcomes the shortcomings of traditional methods, such as long parameter adjustment and update cycles and the inability to effectively suppress dimensional deviations in a timely manner, ensuring high-precision and high-quality machining even under complex machining conditions.

[0082] Finally, the overall technical solution of this application avoids the problem of manual intervention by operators due to misjudgment, which could disrupt the system's optimized matching relationship. Through automated and intelligent parameter adjustment, it can always maintain the optimal processing state, improving processing efficiency and workpiece quality stability.

[0083] In summary, the machine tool machining parameter adjustment method of this application, through a combination of forward detection, pre-adjustment, and adaptive feedback, effectively solves the problems of tool damage, accuracy reduction, and quality caused by local material inhomogeneity in traditional machining. It significantly improves the intelligence level and production efficiency of machine tool machining and can effectively solve problems such as tool damage, machining accuracy reduction, and surface quality deterioration caused by local uneven hardness of workpiece material in traditional machine tool machining.

[0084] In some embodiments, the acoustic wave transmitting and receiving device includes a pair of piezoelectric transducers, which serve as a transmitter and a receiver, respectively, fixed near the machine tool spindle tool holder and kept in a relatively fixed position relative to the tool, facing the workpiece material surface to be cut, and the acoustic wave is an ultrasonic pulse with a frequency in the range of 1 MHz to 10 MHz.

[0085] Specifically, the aforementioned acoustic wave transmitting and receiving device and the implementation method of the acoustic waves can be further refined. The pair of piezoelectric transducers can be understood as two independent piezoelectric crystals or piezoelectric ceramic elements, capable of converting electrical energy into acoustic energy and vice versa. One piezoelectric transducer is configured as a transmitter, responsible for converting electrical pulse signals into acoustic pulses and transmitting them to the workpiece material; the other piezoelectric transducer is configured as a receiver, responsible for receiving the acoustic pulses reflected from the workpiece material and converting them back into electrical signals. This separate transmitting and receiving design helps optimize the signal transmission and reception paths, reduce crosstalk, and improve the signal-to-noise ratio.

[0086] The piezoelectric transducer is fixed near the machine tool spindle shank and in a relatively fixed position relative to the tool. This fixing method ensures that the acoustic wave transmitting and receiving device can always move synchronously with the tool, thereby achieving accurate detection of the workpiece material in front of the tool cutting. By facing the surface of the workpiece material to be cut, it can be ensured that the acoustic waves can directly act on the area to be cut, thereby obtaining real-time acoustic information of that area. The acoustic waves are limited to ultrasonic pulses with a frequency range of 1 MHz to 10 MHz. Ultrasonic waves have a short wavelength and good directionality, enabling them to penetrate the interior of materials and be sensitive to changes in the microstructure of materials. The frequency range of 1 MHz to 10 MHz was selected based on the need to detect internal defects and hardness changes in metallic or composite materials. Ultrasonic waves in this frequency range have suitable penetration depth and resolution in these materials and can effectively reflect the local acoustic impedance characteristics of the materials.

[0087] The technical solution of this application employs a pair of piezoelectric transducers as acoustic wave transmitting and receiving devices, and uses ultrasonic pulses within a specific frequency range to achieve more accurate and stable real-time inference of the local acoustic impedance of workpiece materials. When the ultrasonic pulse emitted by the transmitter penetrates the workpiece material, its propagation characteristics (e.g., attenuation, frequency shift, phase change) are affected by the local acoustic impedance of the material. When the ultrasonic wave encounters an abnormal hardness region within the material, its propagation speed and energy attenuation change significantly, leading to corresponding changes in the acoustic characteristics of the echo signal. The receiver captures these echo signals and converts them into electrical signals for analysis. By extracting and analyzing these acoustic characteristics, the local acoustic impedance of the workpiece material can be accurately inferred, thereby identifying potential local abnormal regions.

[0088] By employing a pair of piezoelectric transducers as independent transmitters and receivers, and fixing them near the machine tool spindle shank to maintain a relatively fixed position relative to the tool, the high synchronization and accuracy of acoustic detection and the cutting process can be ensured, avoiding misjudgments caused by inaccurate detection positioning. Furthermore, limiting the acoustic waves to ultrasonic pulses with frequencies in the range of 1 MHz to 10 MHz allows the detection system to have higher sensitivity and resolution to microstructural changes and abnormal hardness areas within the workpiece material. This enables more accurate and reliable real-time inference of the local acoustic impedance of the workpiece material, providing more precise data support for subsequent pre-adjustment and adaptive adjustment of machining parameters, effectively improving the intelligence and safety of the machining process.

[0089] In some embodiments, the acoustic features include sound wave attenuation, frequency shift, and phase change, and the step of extracting acoustic features from the echo signal includes:

[0090] The acoustic features are extracted by calculating the acoustic attenuation rate based on the echo signal using a high-speed analog-to-digital converter and a digital signal processor, performing a fast Fourier transform to analyze the frequency shift, and analyzing the phase difference.

[0091] Specifically, sound wave attenuation refers to the weakening of the amplitude or intensity of sound waves as they propagate through a medium due to energy dissipation. Within a material, the degree of sound wave attenuation is closely related to factors such as the material's density, elastic modulus, grain structure, and internal defects. By calculating the sound wave attenuation rate, the loss of sound wave energy can be quantified, thus reflecting the material's uniformity and internal structural state.

[0092] Frequency shift refers to the change in the frequency of a sound wave relative to its original transmission frequency during propagation. This shift can be caused by the Doppler effect, the nonlinear response of materials, or changes in the speed of sound. By performing Fast Fourier Transform analysis on the echo signal, this frequency shift can be accurately identified and quantified, thereby revealing changes in the microstructure or stress state of the material.

[0093] In practical applications, phase change refers to the change in the phase of a sound wave relative to the original emitted wave during propagation. Phase change is highly sensitive to variations in sound velocity within materials and interface reflections. By analyzing the phase difference between the echo signal and the emitted signal, detailed information about the material's sound velocity, thickness, and the location of internal defects can be obtained.

[0094] By employing the aforementioned technical solution, which utilizes three complementary acoustic features—sound wave attenuation, frequency shift, and phase change—and combining them with a high-speed analog-to-digital converter and digital signal processor for precise extraction, the inference of local acoustic impedance of the workpiece material becomes more comprehensive and accurate. This comprehensive analysis of multi-dimensional features significantly improves the accuracy and sensitivity of identifying local anomalies within the material, especially in detecting minute defects or changes in hardness gradients. It provides more reliable early warning information, thus offering a more solid data foundation for subsequent pre-adjustment and adaptive adjustment of machining parameters, effectively preventing accelerated tool wear or decreased machining quality caused by local material anomalies.

[0095] In some embodiments, the step of pre-adjusting machining parameters based on a warning signal before the tool contacts the local abnormal area includes:

[0096] Micro-area optical spectroscopy detection was performed in the close proximity region of the cutting edge of the tool to obtain the spectral peak position shift and the change in spectral peak intensity ratio in the spectral data;

[0097] Analyze the spectral peak position shift and the change in spectral peak intensity ratio to identify the phase transition induced by cutting stress;

[0098] Based on the phase transformation induced by the cutting stress, the degree of change in the actual hardness of the material during the cutting process is obtained, and the actual hardness value is obtained.

[0099] Based on the actual hardness value, calculate the target feed rate and speed transition curve parameters;

[0100] Adjust the toolpath based on the target feed rate and the speed transition curve parameters.

[0101] Specifically, before the cutting tool contacts a localized anomalous region of the workpiece material, high-resolution spectral detection technology is used to perform non-contact, in-situ material property analysis in that region, achieving micro-area optical spectral detection. This detection aims to obtain information on the microstructure and stress state of the material just before cutting. The shifts in spectral peak positions and changes in peak intensity ratios in the spectral data can be understood as shifts in the positions (peak positions) of characteristic peaks in the Raman or fluorescence spectra of the material when subjected to external stress or changes in internal structure, and changes in the relative intensities (peak intensity ratios) between different characteristic peaks. These changes directly reflect microscopic changes in the material's crystal structure, chemical bond state, or phase transitions.

[0102] Furthermore, by performing professional analysis on the detected spectral data, such as comparing it with standard spectra or using machine learning algorithms for pattern recognition, it is possible to identify phase transitions induced by cutting stress, thereby determining whether the material has undergone a crystal structure transformation (i.e., a phase transition) caused by cutting stress. Such phase transitions are usually accompanied by significant changes in the material's hardness, toughness, and other mechanical properties.

[0103] Once a phase transition is identified, the actual change in material hardness under cutting stress can be quantified based on a pre-established model relating phase transitions to hardness changes, thus yielding the actual hardness value of the local anomaly region. This actual hardness value is more accurate than simple acoustic impedance inference because it directly reflects the material's true mechanical response at the cutting edge.

[0104] In practical applications, based on the obtained accurate actual hardness value, combined with the machine tool's machining capabilities, cutting tool performance, and required machining quality, the optimal feed rate (target feed rate) for the tool when cutting this local abnormal area, as well as the speed change trajectory (speed transition curve parameters) of the tool when entering or leaving the abnormal area from the normal area, are determined through a preset algorithm or lookup table. These parameters are designed to ensure a smooth transition in the cutting process and avoid impacts.

[0105] Therefore, the calculated target feed rate and speed transition curve parameters are input into the CNC system of the machine tool, which will dynamically modify the tool's motion trajectory and speed control strategy accordingly. This allows the tool to cut in a way that best suits the material properties of the area when it approaches, passes through, or leaves a local abnormal region, thereby optimizing the machining process.

[0106] The technical solution of this application solves the problem of insufficient understanding of the actual mechanical properties of local abnormal regions of materials when pre-adjusting machining parameters by introducing micro-area optical spectroscopy detection technology. Specifically, although acoustic wave transmitting and receiving devices can identify local abnormal regions and infer their acoustic impedance, changes in acoustic impedance may be caused by a variety of factors and do not always directly correspond to the actual hardness changes of the material under cutting stress. By performing micro-area optical spectroscopy detection in the close proximity of the cutting edge of the tool, more refined information about the microstructure of the material can be obtained, namely, changes in spectral peak position shift and spectral peak intensity ratio. These spectral features are a direct reflection of the internal lattice structure, chemical bond state, and stress state of the material.

[0107] It is precisely the ability to accurately analyze these spectral characteristics that makes it possible to identify phase transitions induced by cutting stress. A phase transition is a crystal structure transformation that occurs in a material under specific stress or temperature conditions, usually accompanied by a significant change in material hardness. By identifying and quantifying this phase transition, the actual degree of hardness change during cutting can be obtained more accurately, yielding a more reliable actual hardness value than that inferred from acoustic impedance. Based on this more accurate actual hardness value, the machine tool control system can calculate more reasonable target feed rates and speed transition curve parameters. For example, for regions with significantly increased hardness, the feed rate can be appropriately reduced and a smooth speed transition curve designed to reduce tool impact and wear. Ultimately, by adjusting the toolpath, the tool can cut in a manner most adapted to the local material properties, effectively avoiding problems such as machining instability, tool damage, or decreased machining quality caused by sudden changes in hardness.

[0108] Through the above technical solutions, this application can significantly improve the accuracy and adaptability of machine tool machining parameter pre-adjustment. Compared with traditional methods that rely solely on acoustic impedance for rough judgment, this application, through micro-area optical spectroscopy detection, can deeply understand the microstructural changes and actual hardness response of materials under cutting stress, thereby achieving precise quantification of material properties in local abnormal areas. This precise material information makes the pre-adjustment of machining parameters more scientific and reasonable, effectively avoiding problems such as tool overload, chipping, machining vibration, and surface quality degradation caused by sudden changes in material hardness. In addition, by calculating the target feed rate and speed transition curve parameters and adjusting the tool path, this application ensures a smooth transition of the tool when entering and leaving abnormal areas, greatly improving the stability and reliability of the machining process, extending tool life, and ultimately improving workpiece machining quality and production efficiency.

[0109] In some embodiments, the step of continuously monitoring the real-time feedback signal of the cutting process and further adjusting the machining parameters based on the real-time feedback signal and the early warning signal includes:

[0110] Monitor real-time feedback signals during the cutting process;

[0111] The real-time feedback signal is filtered to obtain the target real-time feedback signal;

[0112] Based on the real-time feedback signal from the target and the early warning signal, abnormal patterns in the processing status are identified, and identification results are obtained.

[0113] Based on the identification results, a parameter adjustment instruction is generated, and the processing parameters are further adjusted according to the parameter adjustment instruction.

[0114] Specifically, during the cutting process, real-time feedback signals of the cutting process are monitored, and physical quantity signals related to the cutting state are collected in real time through various sensors. These signals may include, but are not limited to, cutting force signals, vibration signals, acoustic emission signals, spindle power signals, tool temperature signals, and workpiece surface roughness signals. The purpose is to obtain immediate information on the current cutting state, providing a data basis for subsequent analysis and adjustments.

[0115] The process involves preprocessing the raw, acquired real-time feedback signals to remove noise and interference, and to extract key features. For example, digital filters (such as low-pass and band-pass filters) can be used to remove high-frequency noise or vibration interference at specific frequencies. Alternatively, signal processing techniques such as wavelet transform and Fourier transform can be used to extract characteristic parameters closely related to the cutting state from the raw signal, such as vibration amplitude, frequency components, and force fluctuation range. These processed, more representative signals constitute the target real-time feedback signal. The aim is to improve the signal-to-noise ratio and ensure the accuracy of subsequent anomaly pattern recognition.

[0116] In practical applications, the filtered real-time feedback signal is comprehensively analyzed together with the warning signal inferred from acoustic impedance to identify abnormal patterns in the machining state and obtain the identification result. The warning signal provides prior knowledge of the location of local abnormal areas in the workpiece material and the degree of hardness abnormality, while the target real-time feedback signal reflects the dynamic response when the tool and workpiece actually come into contact. By combining the two, for example, using machine learning algorithms (such as support vector machines and neural networks) or rule-based expert systems, these multi-source data can be fused and analyzed to identify specific abnormal patterns in the machining state, such as tool wear, chip clogging, chatter, and material tearing. The identification result can be a classification label (such as "slight wear" or "severe chatter") or a quantitative index of the degree of abnormality. The purpose is to accurately diagnose the current machining state and provide a basis for decision-making in subsequent parameter adjustments.

[0117] Furthermore, after identifying a specific abnormal pattern, parameter adjustment instructions are generated based on a preset adjustment strategy or optimization algorithm to address that pattern. For example, if tool wear is detected, the instructions might include reducing the feed rate, increasing the spindle speed, or adjusting the depth of cut; if chatter is detected, the instructions might include changing cutting parameters to avoid the resonant frequency. These instructions are then sent to the machine tool control system to adjust the machine tool's machining parameters in real time, thereby restoring the machining process to a normal or optimized state. The aim is to achieve closed-loop control and adaptive optimization of the machining process, ensuring machining quality and efficiency.

[0118] Through the above technical solution, this application significantly enhances the intelligence and adaptability of machine tool machining parameter adjustment. Compared to technical solutions that rely solely on early warning signals for preliminary adjustments or simple monitoring of real-time feedback signals, this application achieves accurate identification of abnormal machining patterns through in-depth processing of real-time feedback signals and collaborative analysis with early warning signals. This enables the generation of more precise and targeted parameter adjustment commands. Consequently, it not only effectively avoids problems such as premature tool wear and decreased workpiece surface quality caused by improper machining parameters, but also promptly responds to and corrects various dynamic anomalies during cutting, significantly improving the stability and reliability of the machining process. This refined adaptive adjustment mechanism ultimately helps extend tool life, improve machining efficiency and product quality, and reduce scrap rates, providing strong support for achieving high-precision, high-efficiency intelligent manufacturing.

[0119] In some alternative embodiments, assuming that when machining an alloy material with localized hardness unevenness, a sound wave is emitted towards the workpiece material in front of the cutting tool via a sound wave transmitting and receiving device, and the echo signal is received. Acoustic features are extracted to infer the existence of a region in front where the local acoustic impedance is consistently higher than the normal reference value, and a warning signal containing the location information and degree of hardness abnormality of this region is generated. Before the tool contacts this region, the feed rate and spindle speed are pre-adjusted based on this warning signal.

[0120] When the cutting tool begins cutting the localized abnormal area, the real-time feedback signal of the cutting process is continuously monitored. Specifically, the cutting force signal is acquired in real time by a three-axis force sensor mounted on the machine tool spindle, and the tool vibration signal is acquired in real time by an accelerometer mounted near the tool holder. These raw cutting force and vibration signals are sent to a digital signal processor for filtering. For example, the cutting force signal is filtered to remove high-frequency noise, and its mean and fluctuation range are extracted as the target real-time feedback signal; the vibration signal is analyzed by fast Fourier transform to extract its spectrum, and the main vibration frequency and amplitude are extracted as the target real-time feedback signal.

[0121] Subsequently, these real-time feedback signals from the targets are comprehensively analyzed together with the previously generated early warning signals. For example, if the early warning signal indicates that the hardness of the area is abnormally high, and the real-time cutting force signal shows that the cutting force continues to exceed the normal range, while the vibration signal shows that the vibration amplitude at a specific frequency has increased significantly, then the current machining state is identified as an abnormal mode of "tool overload and chatter".

[0122] Based on this identification result, parameter adjustment instructions are immediately generated. For example, the instructions might include further reducing the feed rate by 10% from the current level, while simultaneously fine-tuning the spindle speed by 5% to avoid resonance frequencies. These instructions are sent to the machine tool's CNC system, and the machine tool controller adjusts the motion parameters of the feed axis and spindle in real time according to the instructions. In this way, even if new or more complex anomalies occur during the cutting process after pre-adjustment, timely and accurate secondary adjustments can be made, thereby effectively controlling the cutting process and avoiding tool damage and workpiece quality loss.

[0123] In some embodiments, the step of performing micro-area optical spectral detection in a region closely adjacent to the cutting edge of the tool to obtain the spectral peak position shift and the change in spectral peak intensity ratio in the spectral data includes:

[0124] Laser pulses are emitted using an ultrashort pulse laser;

[0125] Establish a clock synchronization mechanism between the laser pulse trigger signal and the gated detector to achieve precise synchronization between laser pulse emission and detector activation;

[0126] Based on the precise synchronization of laser pulse emission and detector activation, the gated detector is activated to collect the optical signal within a very short time after the laser pulse reaches the material and generates a Raman scattering signal.

[0127] The gated detector is turned off at other times after laser pulse excitation.

[0128] The optical signals collected by the gated detector are analyzed for spectral peak position shift and spectral peak intensity ratio change to obtain the spectral peak position shift and spectral peak intensity ratio change in the spectral data.

[0129] Specifically, laser pulses are emitted using lasers with pulse widths on the order of picoseconds (ps) or femtoseconds (fs). These lasers can provide extremely high instantaneous peak power, thereby exciting materials to generate Raman scattering signals in a very short time while minimizing thermal impact on the materials. Establishing a clock synchronization mechanism between the laser pulse trigger signal and the gated detector can be understood as ensuring a precise time correspondence between the moment the laser pulse is emitted and the moment the gated detector turns on to receive the light signal, through hardware or software methods. For example, high-precision clock sources, programmable delay generators, or high-speed synchronization controllers can be used to achieve synchronization accuracy at the nanosecond or even picosecond level. The goal is to ensure that the detector only turns on within the time window during which the effective signal (i.e., the Raman scattering signal) is generated and reaches the detector, thereby effectively suppressing background noise and ambient light interference. In practical applications, the gated detector remains on only after the time delay required for the Raman scattering light to propagate from the material surface to the detector following laser pulse excitation, and for a very short period during which the scattered light signal persists. For example, this on-time window can be set to tens to hundreds of nanoseconds to fully capture the Raman scattering signal. At other times after laser pulse excitation, the gated detector is turned off to further eliminate interference from non-target signals such as cutting fluid fluorescence, chip reflection light, and ambient stray light, significantly improving the signal-to-noise ratio. A detailed analysis is then performed on the purified Raman scattering spectra after time-gated screening to accurately identify spectral characteristic changes caused by material phase transitions induced by cutting stress.

[0130] The above technical solution significantly improves the accuracy and reliability of micro-area optical spectral detection. Compared to traditional spectral detection methods that do not employ time-gated technology, this solution effectively suppresses interference from background noise such as cutting fluid fluorescence, chip reflection light, and ambient stray light, ensuring the purity of the acquired spectral data. This allows for more precise identification of cutting stress-induced phase transitions, leading to more accurate actual material hardness values. This high-precision detection capability enables the pre-adjustment of machine tool processing parameters based on more realistic and detailed material state information, effectively avoiding problems such as accelerated tool wear and decreased processing quality caused by abnormal hardness areas, significantly improving processing efficiency and workpiece quality.

[0131] In some alternative embodiments, it is assumed that when machining high-strength steel, it is necessary to accurately detect the material hardness changes in the immediate vicinity of the cutting edge of the tool. First, a femtosecond laser is used as an ultrashort pulse laser with a pulse width of 100 femtoseconds and a repetition frequency of 80 MHz. Simultaneously, an enhanced charge-coupled device (ICCD) with nanosecond-level gating capability is configured as a gated detector. A high-precision digital delay generator precisely synchronizes the trigger signal of the femtosecond laser with the gate opening signal of the ICCD, ensuring that the ICCD is turned on for approximately 20 nanoseconds after the laser pulse excites the material, following a time-of-flight delay of approximately 5 nanoseconds, to capture Raman scattered light. Outside this time window, the ICCD remains off. When the laser pulse irradiates the surface of the workpiece material and generates a Raman scattering signal, the ICCD collects the light signal only within the preset 20 nanosecond window. For example, when a specific Raman peak (such as a characteristic peak of the carbide phase) is detected to shift towards a higher wavenumber, and its intensity changes significantly relative to the characteristic peak intensity of the matrix phase, it can be determined that a cutting stress-induced phase transition exists in that region, indicating a local increase in the material's hardness. In this way, even in complex environments with splashing cutting fluid and high-speed chip movement, clear and interference-free Raman spectra can be obtained, thereby accurately assessing the actual hardness changes of the material.

[0132] In some embodiments, the step of performing micro-area optical spectral detection in a region closely adjacent to the cutting edge of the tool includes:

[0133] A high-speed airflow jet device is deployed along the path of the laser pulse emitted by the laser.

[0134] Within 0.5 to 0.1 milliseconds before the laser pulse is emitted, the high-speed airflow jet device sprays high-pressure inert gas into the laser beam propagation path to form a locally clean channel free of chips and liquid film.

[0135] A high-speed airflow jet device is deployed along the path of the fiber optic probe receiving Raman scattered light;

[0136] Within 0.5 to 0.1 milliseconds before the gated detector is activated, the high-speed airflow jet device injects high-pressure inert gas into the optical signal return path to form a locally clean channel free of chips and liquid film.

[0137] Transparent protective windows are installed at both the laser transmitter and the fiber optic probe receiver.

[0138] A miniature heating unit is provided inside the transparent protective window, and the surface temperature of the transparent protective window is maintained above the boiling point of the cutting fluid or the melting point of the chips by means of the miniature heating unit.

[0139] Specifically, a high-speed airflow jet device is deployed along the path of the laser pulse emitted by the laser to remove chips and liquid films from the laser propagation path before the laser pulse reaches the workpiece material. This high-speed airflow jet device can be understood as a device capable of instantaneously jetting high-pressure inert gas, for example, a pneumatic system equipped with precision solenoid valves and nozzles. Within 0.5 to 0.1 milliseconds before laser pulse emission, the high-speed airflow jet device jets high-pressure inert gas into the laser beam propagation path, forming a locally clean channel free of chips and liquid films. The purpose is to ensure that the laser pulse propagates in a clean environment, avoiding scattering or attenuation due to medium interference. This 0.5 to 0.1 millisecond time window is precisely calculated and experimentally verified to ensure that the clean channel is fully established before the laser pulse arrives and dissipates rapidly after the laser pulse passes, thus avoiding unnecessary impact on subsequent cutting processes.

[0140] Similarly, a high-speed airflow jet device is deployed along the path of the fiber optic probe receiving Raman scattered light to remove debris and liquid film from the optical signal return path before the Raman scattered light returns to the fiber optic probe. Within 0.5 to 0.1 milliseconds before the gated detector opens, the high-speed airflow jet device injects high-pressure inert gas into the optical signal return path, creating a locally clean channel free of debris and liquid film. This ensures that the optical signal can be transmitted clearly and without interference when the gated detector receives the Raman scattered signal.

[0141] In addition, transparent protective windows are installed at both the laser transmitter and the fiber optic probe receiver. These windows physically isolate the optical components from the harsh cutting environment, preventing direct contact and damage to precision optical parts by chips and cutting fluid. The transparent protective windows are typically made of high-transmittance, high-hardness materials, such as sapphire or special quartz glass. A miniature heating unit is installed inside the transparent protective window. This unit maintains the surface temperature of the window above the boiling point of the cutting fluid or the melting point of the chips. This actively prevents the cutting fluid from condensing and forming a liquid film on the window surface, or prevents melted chips from adhering to the window surface, thus continuously maintaining the cleanliness and optical performance of the window. The miniature heating unit can take the form of a resistance heating wire, a transparent conductive film heating element, or a miniature thermoelectric module.

[0142] The technical solution of this application employs a high-speed airflow jet device for instantaneous and precisely timed local cleaning along the critical paths of laser emission and scattered light reception. This effectively removes chips and liquid films generated during the cutting process, providing a clear optical path for laser pulse propagation and Raman scattered light collection. Simultaneously, by installing a transparent protective window at the end of the optical element, combined with an internal micro-heating unit, condensation of cutting fluid and adhesion of chips are actively prevented, ensuring the long-term cleanliness and light transmittance of the window from both physical and thermodynamic perspectives. This ensures stable and accurate micro-area optical spectroscopy detection under harsh machine tool processing environments, providing reliable spectral data for subsequent cutting stress-induced phase transformation analysis and actual hardness value acquisition.

[0143] The above technical solutions significantly improve the reliability and accuracy of micro-area optical spectral detection in machine tool processing environments. The instantaneous cleaning function of the high-speed airflow jet device effectively solves the problem of interference from chips and liquid films on the optical path, ensuring the complete transmission of laser signals and the effective reception of scattered signals. The transparent protective window and its heating unit fundamentally avoid contamination and damage to optical components, extending the equipment's service life and maintaining detection accuracy. These improvements enable clearer and more accurate data from spectral detection in the close proximity of the cutting edge, allowing for more precise identification of cutting stress-induced phase transitions and the acquisition of the actual hardness changes of the material during cutting. This provides a solid data foundation for the pre-adjustment of subsequent processing parameters, effectively improving processing stability and efficiency.

[0144] In some alternative embodiments, it is assumed that during precision milling of high-hardness alloy materials, it is necessary to monitor the material hardness change in front of the cutting edge in real time. To achieve this, a micro-area optical spectroscopy detection system is installed near the machine tool spindle holder. This system includes an ultrashort pulse laser, a fiber optic probe, and a gated detector. To ensure that the laser beam and scattered light are not interfered with by chips and cutting fluid during transmission, sapphire transparent protective windows are installed at the laser emitter and fiber optic probe receiver. Inside each window, a miniature resistance heating unit is integrated, which maintains the window surface temperature at 120°C, higher than the boiling point of commonly used cutting fluids, by precisely controlling the current. Simultaneously, high-speed airflow jet devices are deployed along the laser beam propagation path and the fiber optic probe receiving path. When the laser is ready to emit a laser pulse, 0.2 milliseconds before the laser pulse is emitted, the high-speed airflow jet devices inject high-pressure nitrogen gas into the laser beam path for approximately 0.1 milliseconds, creating a locally clean channel free of chips and liquid film. Subsequently, the laser pulse passes through this channel, exciting the material to produce Raman scattering. 0.2 milliseconds before the gated detector opens, another high-speed airflow jet injects high-pressure nitrogen gas into the optical signal return path for approximately 0.1 milliseconds, ensuring that the scattered light can be clearly received by the fiber optic probe and transmitted to the gated detector. In this way, high-quality spectral data can be continuously obtained even in harsh environments with high-speed cutting and large amounts of cutting fluid spray, thereby accurately assessing local hardness changes in the material.

[0145] In some embodiments, the step of installing transparent protective windows at the laser transmitter and the fiber optic probe receiver respectively further includes:

[0146] The surface of the transparent protective window is laser polished and a replaceable sacrificial layer is provided so that the sacrificial layer is replaced when the wear of the sacrificial layer reaches a preset threshold.

[0147] Vibration and temperature sensors are integrated into the mounting base of the transparent protective window to monitor the vibration state and temperature changes of the transparent protective window.

[0148] Adjust the processing parameters when abnormal vibration or temperature occurs.

[0149] Specifically, the laser polishing treatment on the surface of the transparent protective window aims to improve its surface finish, reduce microscopic defects, thereby enhancing its wear and corrosion resistance and optimizing optical transmission performance. The replaceable sacrificial layer can be understood as a protective material attached to the surface of the transparent protective window, designed to preferentially withstand wear and impact in harsh cutting environments, thus protecting the main transparent protective window body. When the wear of this sacrificial layer reaches a preset threshold, such as through optical detection or time counting, it can be easily replaced without replacing the entire transparent protective window, significantly reducing maintenance costs and downtime.

[0150] The vibration and temperature sensors integrated into the mounting bracket of the transparent protective window are used to monitor its operational status in real time. The vibration sensor detects abnormal vibrations caused by chip impact, mechanical vibration, or structural loosening, while the temperature sensor monitors the surface temperature of the transparent protective window, promptly identifying problems such as overheating or abnormal cooling. Data from these sensors is continuously collected and analyzed.

[0151] In practical applications, when abnormal vibration conditions are detected, such as vibration frequency or amplitude exceeding the normal range, or when abnormal temperatures occur, such as a significant increase or decrease in surface temperature exceeding the safety threshold, potential problems or existing damage to the transparent protective window can be immediately identified. Based on these abnormal signals, machining parameters will be adjusted in a timely manner, such as reducing feed rate, depth of cut, or spindle speed, to mitigate the impact on the transparent protective window, prevent further damage, and buy time for subsequent maintenance or replacement.

[0152] The technical solution of this application fundamentally improves the surface quality and damage resistance of the transparent protective window by introducing laser polishing and a replaceable sacrificial layer. Laser polishing makes the window surface smoother, reduces stress concentration points, and thus reduces the destructive effects of chip impact and abrasive wear. The replaceable sacrificial layer provides an active protection mechanism, ensuring that in harsh environments, the sacrificial layer bears the wear first, effectively protecting the core transparent protective window body and extending its service life. When the sacrificial layer wears to a preset threshold, the window's protective performance can be restored through a simple replacement operation, avoiding downtime of the entire detection system due to window damage.

[0153] Furthermore, by integrating vibration and temperature sensors into the mounting base of the transparent protective window, real-time, dynamic monitoring of the window's status is achieved. The vibration sensor can sensitively detect abnormal vibrations caused by chip impact, mechanical loosening, etc., while the temperature sensor can promptly reflect the window's heat dissipation or heating status. This real-time feedback data enables continuous assessment of the transparent protective window's health condition. Once vibration or temperature anomalies are detected, indicating that the window may be facing damage or has already been damaged, a rapid response can be made to mitigate further damage to the window by adjusting machining parameters, such as reducing the cutting load. This effectively avoids detection interruptions or data distortion caused by window damage, ensuring the continuity and accuracy of micro-area optical spectral detection.

[0154] Through the above technical solutions, this application significantly improves the durability, maintainability, and operational reliability of transparent protective windows in machine tool processing environments. Laser polishing effectively enhances the window's wear and corrosion resistance, extending its initial service life. The introduction of a replaceable sacrificial layer allows for rapid localized repair of the window when it suffers wear, significantly reducing maintenance costs and downtime, and improving the overall uptime of the equipment. More importantly, by integrating vibration and temperature sensors, real-time monitoring and early warning of the transparent protective window's status are achieved. When an anomaly is detected, processing parameters can be adjusted promptly, effectively preventing further damage to the window, ensuring the continuity and accuracy of micro-area optical spectral detection, and thus ensuring the overall reliability and precision of the machine tool processing parameter adjustment method.

[0155] In some alternative embodiments, it is assumed that during high-speed milling of a high-hardness alloy steel workpiece, chips impact the transparent protective window at extremely high speeds. To address this challenge, the surface of the transparent protective window is first subjected to fine laser polishing using a nanosecond-level pulsed laser, reducing its surface roughness Ra value to below 0.5 micrometers, significantly improving its impact and wear resistance. On this polished surface, a 50-micrometer-thick diamond-like carbon (DLC) film is grown as a sacrificial layer using chemical vapor deposition (CVD). This DLC sacrificial layer possesses extremely high hardness and wear resistance, effectively resisting chip impact in the initial stages.

[0156] The mounting bracket for the transparent protective window integrates a piezoelectric vibration sensor and a thermocouple temperature sensor. The vibration sensor is configured to monitor abnormal vibrations in the frequency range of 1kHz to 10kHz, with a peak vibration acceleration threshold set at 5g. The temperature sensor monitors the window surface temperature and is set to operate within a normal temperature range of 80°C to 120°C.

[0157] During actual machining, the presence of hard inclusions in the workpiece material may generate abnormally hard chips that impact the transparent protective window with higher energy. In this case, the vibration sensor may detect vibration acceleration peaks exceeding 5g, or the window surface temperature may momentarily rise to 130°C due to localized friction and impact. Once these abnormal signals are identified, the control system immediately determines that the transparent protective window may be suffering excessive wear or damage. Based on this warning, the machine tool's machining parameters are immediately adjusted; for example, the feed rate is reduced from 500mm / min to 300mm / min, and the depth of cut is reduced from 0.5mm to 0.3mm to reduce the impact load on the window. Simultaneously, the operator is prompted to check the wear of the sacrificial layer. When the sacrificial layer wear reaches a preset thickness threshold (e.g., a 20-micron reduction detected by an optical interferometer), a command to replace the sacrificial layer is issued. The operator can quickly replace the DLC sacrificial layer, thus avoiding the need to replace the entire expensive transparent protective window and ensuring the continuous and stable operation of the detection system.

[0158] In some embodiments, the step of performing laser polishing on the surface of the transparent protective window includes:

[0159] A nanosecond-level pulsed laser is used to emit laser pulses, and the laser beam is controlled to scan an area on the surface of the transparent protective window.

[0160] Real-time monitoring of the temperature feedback signal on the surface of the transparent protective window;

[0161] Based on the temperature feedback signal, the laser power and scanning speed are dynamically adjusted.

[0162] Specifically, a laser with a pulse width on the order of nanoseconds is selected as the laser source. This type of laser can provide high energy density and short-duration laser pulses, which is beneficial for achieving precise removal and modification of material surfaces while reducing the heat-affected zone. The laser beam scans the surface of the transparent protective window, which can be understood as using a galvanometer system or a moving platform to make the laser pulse follow a preset path and coverage pattern to process the entire area of ​​the transparent protective window to be polished point by point or line by line, ensuring polishing uniformity.

[0163] During laser polishing, real-time temperature data of the transparent protective window surface under laser irradiation is continuously acquired using a non-contact temperature sensor (such as an infrared thermometer) or other temperature detection units integrated into the polishing equipment. This enables real-time monitoring of the temperature feedback signal of the transparent protective window surface. This temperature feedback signal is a key indicator for evaluating the intensity of the laser-material interaction and the thermal effect.

[0164] In practical applications, based on real-time acquired temperature data, the control system dynamically adjusts the laser's output power and scanning speed in an instantaneous and adaptive manner. For example, when excessively high local temperatures are detected, the laser power can be automatically reduced or the scanning speed increased to prevent material overheating and damage; conversely, when the temperature is too low or the polishing effect is unsatisfactory, the laser power can be appropriately increased or the scanning speed decreased to enhance the polishing effect. The aim is to achieve precise thermal management and energy control of the laser polishing process, ensuring polishing quality, preventing damage, and improving polishing efficiency.

[0165] The technical solution of this application effectively solves the problems of uneven polishing, local overheating, or low efficiency that may exist in traditional laser polishing by introducing a nanosecond-level pulsed laser, area scanning, real-time temperature monitoring, and a mechanism for dynamically adjusting laser parameters. Specifically, the nanosecond-level pulsed laser can provide precise energy input, and combined with area scanning, it ensures the comprehensiveness and uniformity of polishing coverage. More importantly, by monitoring the temperature feedback signal of the transparent protective window surface in real time, the thermal effect of the laser-material interaction can be sensed instantly. It is precisely because of this real-time temperature feedback that the control system can dynamically adjust the laser power and scanning speed according to the actual situation. For example, when a local temperature is detected to be too high, the laser power will be immediately reduced or the scanning speed will be increased to avoid damage or deformation of the material due to overheating; conversely, when the temperature is too low or the polishing effect is insufficient, the laser power will be increased or the scanning speed will be slowed down to ensure that the expected polishing effect is achieved. This closed-loop control mechanism ensures that the laser polishing process is always in an optimal state, thereby overcoming the limitations caused by the lack of precise control.

[0166] Through the above technical solution, this application enables precise and adaptive control of the laser polishing process on the surface of transparent protective windows. Compared with polishing methods lacking real-time feedback and dynamic adjustment, this application can significantly improve the uniformity and consistency of polishing quality, effectively avoid material damage caused by local overheating, and thus extend the service life of the transparent protective window and its sacrificial layer. Furthermore, by optimizing laser parameters, polishing efficiency can be improved, production costs reduced, and the long-term stable operation and data accuracy of the micro-area optical spectral detection system ensured.

[0167] In some alternative embodiments, it is assumed that a transparent protective window surface made of quartz glass needs to be laser polished. First, a nanosecond-level pulsed laser with a pulse width of 10 nanoseconds and a repetition frequency of 100 kHz is used to emit laser pulses. The laser beam passes through a high-speed galvanometer system, scanning a zigzag area on the transparent protective window surface at a speed of 200 mm / s, with a scan line spacing of 50 micrometers. During the polishing process, a non-contact infrared thermometer is deployed near the laser-affected area to monitor the temperature of the transparent protective window surface in real time at a frequency of 1000 times per second. The control system has a preset ideal polishing temperature range of 800°C to 900°C. When the local temperature reported by the infrared thermometer remains above 900°C for 50 milliseconds, the control system automatically reduces the laser power by 10% and increases the scanning speed by 5%; conversely, when the local temperature remains below 800°C for 50 milliseconds, the control system increases the laser power by 5% and reduces the scanning speed by 2%. This dynamic adjustment mechanism ensures that the temperature of the entire polishing area is always maintained within the optimal range, thereby achieving a uniform polishing effect with a surface roughness Ra of less than 5 nanometers, which significantly improves the optical performance and durability of the transparent protective window.

[0168] In modern industrial production, the setting and adjustment of machining parameters on high-precision machine tools directly determine product quality and production efficiency when processing key components. Traditional methods for adjusting machining parameters primarily rely on preset material properties and cutting force feedback mechanisms. When the cutting tool encounters a region of abnormal hardness within the workpiece material, the cutting force increases instantaneously, and the only response is passive, such as reducing the feed rate. This delayed adjustment often results in the tool experiencing excessive stress upon contact with the abnormal region, leading to microscopic chipping or rapid wear, thus affecting machining accuracy and surface quality. Failure to address these issues will result in both machining efficiency and workpiece quality falling short of expectations.

[0169] This application also proposes a machine tool machining parameter adjustment system, such as... Figure 2 As shown, a machine tool machining parameter adjustment system 100 includes:

[0170] The detection module 10 is used to emit sound waves to the workpiece material in front of the cutting tool through a sound wave transmitting and receiving device and receive echo signals, extract acoustic features from the echo signals, and infer the local acoustic impedance of the workpiece material in real time based on the acoustic features.

[0171] The identification module 20 is used to compare the local acoustic impedance with a preset normal reference value. When the local acoustic impedance is found to be continuously higher than the normal reference value and exceeds a preset threshold, it is determined that there is a local abnormal area and a warning signal containing location information and the degree of hardness abnormality is generated.

[0172] Pre-adjustment module 30 is used to pre-adjust machining parameters based on a warning signal before the tool contacts the local abnormal area;

[0173] The adaptive adjustment module 40 is used to continuously monitor the real-time feedback signal of the cutting process and further adjust the machining parameters based on the real-time feedback signal and the early warning signal.

[0174] The machine tool machining parameter adjustment system of this application introduces a detection module to proactively detect workpiece material, enabling it to predict localized hardness anomalies within the material before the tool contacts abnormal areas. The pre-adjustment module then adjusts machining parameters accordingly, effectively avoiding tool damage and machining quality issues caused by passive adjustment after direct tool contact with abnormal areas in traditional methods. Furthermore, by combining an adaptive adjustment module to monitor real-time feedback signals during the cutting process, this system can further adaptively adjust machining parameters, ensuring the stability and efficiency of the machining process. In summary, the machine tool machining parameter adjustment system of this application, through a combination of proactive detection, pre-adjustment, and adaptive feedback, effectively solves the problems of tool damage, accuracy degradation, and quality issues caused by localized material inhomogeneities in traditional machining, significantly improving the intelligence level and production efficiency of machine tool machining.

[0175] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for adjusting machine tool processing parameters, characterized in that, include: A sound wave is emitted to the workpiece material in front of the cutting tool through a sound wave transmitting and receiving device and the echo signal is received. The acoustic features of the echo signal are extracted and the local acoustic impedance of the workpiece material is inferred in real time based on the acoustic features. The local acoustic impedance is compared with a preset normal reference value. When the local acoustic impedance is found to be continuously higher than the normal reference value and exceeds a preset threshold, it is determined that there is a local abnormal area, and a warning signal containing location information and the degree of hardness abnormality is generated. Before the cutting tool contacts the local abnormal area, the machining parameters are adjusted in advance based on the warning signal; Continuously monitor the real-time feedback signals of the cutting process, and further adjust the machining parameters based on the real-time feedback signals and early warning signals; The step of pre-adjusting machining parameters based on a warning signal before the cutting tool contacts the local abnormal area includes: Laser pulses are emitted using an ultrashort pulse laser; Establish a clock synchronization mechanism between the laser pulse trigger signal and the gated detector to achieve precise synchronization between laser pulse emission and detector activation; Based on the precise synchronization of laser pulse emission and detector activation, the gated detector is activated to collect the optical signal within a very short time after the laser pulse reaches the material and generates a Raman scattering signal. The gated detector is turned off at other times after laser pulse excitation. Spectral peak position shift and spectral peak intensity ratio change were analyzed on the optical signal collected by the gated detector to obtain the spectral peak position shift and spectral peak intensity ratio change in the spectral data. Analyze the spectral peak position shift and the change in spectral peak intensity ratio to identify the phase transition induced by cutting stress; Based on the phase transformation induced by the cutting stress, the degree of change in the actual hardness of the material during the cutting process is obtained, and the actual hardness value is obtained. Based on the actual hardness value, calculate the target feed rate and speed transition curve parameters; Adjust the toolpath based on the target feed rate and the speed transition curve parameters.

2. The machine tool machining parameter adjustment method according to claim 1, characterized in that, The acoustic wave transmitting and receiving device includes a pair of piezoelectric transducers, which serve as the transmitter and receiver respectively. They are fixed near the machine tool spindle tool holder and maintain a relatively fixed position with the tool, facing the workpiece material surface to be cut. The acoustic wave is an ultrasonic pulse with a frequency in the range of 1 MHz to 10 MHz.

3. The machine tool machining parameter adjustment method according to claim 1, characterized in that, The acoustic features include sound wave attenuation, frequency shift, and phase change. The step of extracting acoustic features from the echo signal includes: The acoustic features are extracted by calculating the acoustic attenuation rate based on the echo signal using a high-speed analog-to-digital converter and a digital signal processor, performing a fast Fourier transform to analyze the frequency shift, and analyzing the phase difference.

4. The machine tool machining parameter adjustment method according to claim 1, characterized in that, The step of continuously monitoring the real-time feedback signal of the cutting process and further adjusting the machining parameters based on the real-time feedback signal and the early warning signal includes: Monitor real-time feedback signals during the cutting process; The real-time feedback signal is filtered to obtain the target real-time feedback signal; Based on the real-time feedback signal from the target and the early warning signal, abnormal patterns in the processing status are identified, and identification results are obtained. Based on the identification results, a parameter adjustment instruction is generated, and the processing parameters are further adjusted according to the parameter adjustment instruction.

5. The machine tool machining parameter adjustment method according to claim 1, characterized in that, The step of performing micro-area optical spectral detection in the close proximity region of the cutting edge of the tool includes: A high-speed airflow jet device is deployed along the path of the laser pulse emitted by the laser. Within 0.5 to 0.1 milliseconds before the laser pulse is emitted, the high-speed airflow jet device sprays high-pressure inert gas into the laser beam propagation path to form a locally clean channel free of chips and liquid film. A high-speed airflow jet device is deployed along the path of the fiber optic probe receiving Raman scattered light; Within 0.5 to 0.1 milliseconds before the gated detector is activated, the high-speed airflow jet device injects high-pressure inert gas into the optical signal return path to form a locally clean channel free of chips and liquid film. Transparent protective windows are installed at both the laser transmitter and the fiber optic probe receiver. A miniature heating unit is provided inside the transparent protective window, and the surface temperature of the transparent protective window is maintained above the boiling point of the cutting fluid or the melting point of the chips by means of the miniature heating unit.

6. The machine tool machining parameter adjustment method according to claim 5, characterized in that, The step of installing transparent protective windows at the laser transmitter and the fiber optic probe receiver respectively further includes: The surface of the transparent protective window is laser polished and a replaceable sacrificial layer is provided so that the sacrificial layer is replaced when the wear of the sacrificial layer reaches a preset threshold. Vibration and temperature sensors are integrated into the mounting base of the transparent protective window to monitor the vibration state and temperature changes of the transparent protective window. Adjust the processing parameters when abnormal vibration or temperature occurs.

7. The machine tool machining parameter adjustment method according to claim 6, characterized in that, The step of performing laser polishing on the surface of the transparent protective window includes: A nanosecond-level pulsed laser is used to emit laser pulses, and the laser beam is controlled to scan an area on the surface of the transparent protective window. Real-time monitoring of the temperature feedback signal on the surface of the transparent protective window; Based on the temperature feedback signal, the laser power and scanning speed are dynamically adjusted.

8. A machine tool machining parameter adjustment system, characterized in that, The system includes: The detection module is used to emit sound waves to the workpiece material in front of the cutting tool through a sound wave transmitting and receiving device and receive the echo signal, extract acoustic features from the echo signal, and infer the local acoustic impedance of the workpiece material in real time based on the acoustic features. The identification module is used to compare the local acoustic impedance with a preset normal reference value. When the local acoustic impedance is found to be continuously higher than the normal reference value and exceeds a preset threshold, it is determined that there is a local abnormal area and a warning signal containing location information and the degree of hardness abnormality is generated. The pre-adjustment module is used to pre-adjust machining parameters based on a warning signal before the tool contacts the local abnormal area; An adaptive adjustment module is used to continuously monitor real-time feedback signals during the cutting process, and further adjust machining parameters based on the real-time feedback signals and early warning signals; The step of pre-adjusting machining parameters based on a warning signal before the cutting tool contacts the local abnormal area specifically includes: Laser pulses are emitted using an ultrashort pulse laser; Establish a clock synchronization mechanism between the laser pulse trigger signal and the gated detector to achieve precise synchronization between laser pulse emission and detector activation; Based on the precise synchronization of laser pulse emission and detector activation, the gated detector is activated to collect the optical signal within a very short time after the laser pulse reaches the material and generates a Raman scattering signal. The gated detector is turned off at other times after laser pulse excitation. Spectral peak position shift and spectral peak intensity ratio change were analyzed on the optical signal collected by the gated detector to obtain the spectral peak position shift and spectral peak intensity ratio change in the spectral data. Analyze the spectral peak position shift and the change in spectral peak intensity ratio to identify the phase transition induced by cutting stress; Based on the phase transformation induced by the cutting stress, the degree of change in the actual hardness of the material during the cutting process is obtained, and the actual hardness value is obtained. Based on the actual hardness value, calculate the target feed rate and speed transition curve parameters; Adjust the toolpath based on the target feed rate and the speed transition curve parameters.

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